Online DE Optimization for Fuzzy-PID Controller of Semi-Active Suspension System Featuring MR Damper

Abstract

In pursue of a comfortable vehicle driving in different road profiles, intelligent methods used to improve the suspension system of the vehicle. Semi-active suspension system outperformed other suspension systems because it contains an intelligent actuator that can give the appropriate force to dissipate unwanted vibration using intelligent and real-time controllers. Here in this paper MR fluid damper with Fuzzy-PID controller examined to optimized using modified DE algorithm. However, in the Fuzzy-PID controller, the fuzzy logic algorithm is used to autotune the PID controller, but it cannot be considered as a fully real-time controller since the fuzzy algorithm uses a previous knowledge base built offline. The offline design of Fuzzy algorithm cannot cope with real-time unexpected vibrations occurred while driving car in different road profiles. In this paper, a Fuzzy-DE-PID controller proposed based on modified DE algorithm to enhance the Fuzzy logic gains in order to increase vehicle semi-active suspension system performance. To prove the effectiveness of the proposed controllers, a simulation and experimental tests were conducted using the proposed controller with different disturbances. The results of the simulation and the experimental tests for the proposed controller showed improvement of the vehicle's ride comfort over the Fuzzy-PID and the passive system. We believe that using this proposed controller in any other real-time application will improve the performance to the highest levels without the need for a previous knowledge base for designing a realtime Fuzzy-PID controller.

Online optimal tuning of fuzzy PID controller using grey wolf optimizer for quarter car semi-active suspension system

“…Table 2 lists the GWO's parameters. 25 Simulation and analysis Simulation model. Utilizing Matlab/Simulink, the simulation model is implemented (Mathworks, R2022a, Massachusetts, USA).…”

“…Table 3 is a list of the suspension system's specifications. 25 Table 4 lists the modified Bouc-Wen model's parameters. The suggested GWO algorithm would compare the suspension system error with the best GWO findings every time in order to aid in forecasting the best PID settings.…”

“…According to the Gaussian membership design, the K p , K i , and K d output of the fuzzy logic inference system are similarly divided into the three states of Big (B), Medium (M), and Small (S). 25 Each output PID parameter has 3 × 3 = 9 rules, as indicated in Table 1, depending on the number of fuzzy variables in the input-output fuzzy sets.…”

Particle swarm optimized fuzzy proportional-integral-derivative controller-based transverse leaf spring active suspension for vibration control

“…In recent years, semi-active and active control strategies for different suspension types have become increasingly mature, such as the Skyhook control, 13 the sliding mode control, 14 the neural network, 15 the fuzzy logic, 16 etc. The PID controller is widely used because of its simple operation, strong applicability, and low cost.…”

Firefly Algorithm Optimization-Based LQR Controller for 1/4 Vehicle Active Suspension System: Design and Performance Evaluation

“…PSO is one of the most commonly used metaheuristic methods in various applications since its invention in the mid-1990s (Gad, 2022), such as LQR and PID controller parameterization compared with double-tank control (Selamat et al, 2015), stabilization of a two-wheel chair using LQR controller (Aula et al, 2015), and optimal position control of a permanent magnet DC motor compared with the famous ACO (Ant Colony Optimizer) metaheuristic method (Rasheed, 2020). Another notable method is DE, used for PID-Fuzzy controller optimization applied to a semi-active suspension system with Magneto-Rheological damper (AHMED, et al, 2022), as well as new methods like the Aquila optimizer used for shifting nonlinear phenomena in boost converters (Korich, et al, 2023).…”